system

Abstract

The system of the embodiment aims to appropriately match in-house technology and know-how with external needs and support project creation. [Solution] The system according to the embodiment comprises a collection unit, a storage unit, an analysis unit, and a support unit. The collection unit collects data on in-house technology and know-how. The storage unit stores the data collected by the collection unit in a vector database. The analysis unit analyzes the data stored by the storage unit and performs appropriate matching. The support unit supports project development based on the matching results obtained by the analysis unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there was an issue of inefficient matching of in-house technology and know-how with external needs, and insufficient support for project implementation.

[0005] The system of the embodiment aims to appropriately match in-house technology and know-how with external needs and support project creation. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a storage unit, an analysis unit, and a support unit. The collection unit collects data on in-house technology and know-how. The storage unit stores the data collected by the collection unit in a vector database. The analysis unit analyzes the data stored by the storage unit and performs appropriate matching. The support unit supports project development based on the matching results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately match in-house technology and know-how with external needs, and support project creation. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A project development support system according to an embodiment of the present invention uses a generative AI-based vector database to match internal technology and know-how within a large company with external needs and support project development. The project development support system collects data on internal technology and know-how and stores it in the vector database. Next, it collects data on external needs and stores it in the vector database. The generative AI analyzes this data and performs optimal matching. Based on the matching results, it supports project development. This system breaks down the siloed structure within large companies and enables effective utilization of internal technology and know-how. Furthermore, as the number of projects increases, companies are expected to become innovation hubs for new businesses. For example, the project development support system collects data on internal technology and know-how and stores it in the vector database. In this process, detailed technical information and know-how from each department is collected and stored in the vector database. For example, documents, reports, and research data related to specific technologies are collected. Next, the project development support system collects data on external needs and stores it in the vector database. For example, it collects technology needs from external companies, market research data, and customer feedback. The generative AI analyzes this data and performs optimal matching. The generative AI analyzes data stored in a vector database to find the optimal combination of in-house technology and know-how with external needs. For example, if a specific technology matches the needs of an external company, it will be turned into a project. The project creation support system then supports project creation based on the matching results. Specifically, it supports project progress management, outcome evaluation, resource allocation, and more. For example, it monitors project progress in real time and makes adjustments as needed. This system breaks down the siloed structure within large companies and enables the effective use of in-house technology and know-how. Furthermore, as the number of projects increases, companies are expected to become innovation hubs for new businesses. This allows the project creation support system to optimally match in-house technology and know-how with external needs and support project creation.

[0029] A project support system according to an embodiment includes a collection unit, a storage unit, an analysis unit, and a support unit. The collection unit collects data on in-house technology and know-how. Examples of in-house technology and know-how include, but are not limited to, specific technical fields, business processes, and specialized knowledge. The collection unit collects, for example, technical information and know-how from each department. The collection unit can also collect technology needs and market research data from external companies. For example, the collection unit can collect technology needs and market research data from external companies, customer feedback, and the like. The storage unit stores the data collected by the collection unit in a vector database. Examples of the vector database include, but are not limited to, text data, numerical data, and image data. The storage unit stores, for example, the collected technical information and know-how in the vector database. The storage unit can also store collected data on external needs in the vector database. The analysis unit analyzes the data stored in the vector database and performs optimal matching. The analysis unit, for example, uses a generative AI to find the optimal combination of in-house technology and know-how and external needs. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI calculates the similarity between in-house technology and know-how and external needs to perform optimal matching. The analysis unit can also use the generation AI to analyze the relevance between in-house technology and know-how and external needs. The support unit supports project creation based on the matching results obtained by the analysis unit. The support unit, for example, manages project progress and evaluates results. The support unit can also allocate project resources. For example, the support unit monitors the progress of the project in real time and makes adjustments as necessary. As a result, the project creation support system according to the embodiment can optimally match in-house technology and know-how with external needs to support project creation.

[0030] The collection unit can collect technical information and know-how from each department. The collection unit, for example, collects technical information and know-how from each department. The technical information and know-how includes, for example, specific technical fields, business processes, and specialized knowledge, but is not limited to these examples. The collection unit, for example, evaluates the impact of the technical information from each department and prioritizes collecting information with high impact. The collection unit can also evaluate the uniqueness of the know-how from each department and prioritize collecting know-how with high uniqueness. Furthermore, the collection unit can evaluate the update frequency of the technical information from each department and prioritize collecting information with high update frequency. This allows for effective collection of technical information and know-how from each department.

[0031] The collection unit can collect technology needs and market research data from external companies. The collection unit, for example, collects technology needs and market research data from external companies. The technology needs and market research data include, but are not limited to, specific technology fields, market trends, and customer needs. The collection unit, for example, collects technology needs from external companies and stores them in a vector database. The collection unit can also collect market research data and store it in the vector database. Furthermore, the collection unit can collect feedback from customers and store it in the vector database. This makes it possible to effectively collect technology needs and market research data from external companies.

[0032] The analysis unit can analyze data stored in the vector database to find an appropriate combination of in-house technology and know-how and external needs. The analysis unit can, for example, analyze data stored in the vector database to find an appropriate combination of in-house technology and know-how and external needs. Examples of appropriate combinations include, but are not limited to, similarity calculation methods and matching algorithms. The analysis unit can, for example, use a generation AI to calculate the similarity between in-house technology and know-how and external needs. The analysis unit can also use the generation AI to analyze the relevance between in-house technology and know-how and external needs. Furthermore, the analysis unit can also use the generation AI to find the optimal combination of in-house technology and know-how and external needs. This makes it possible to find the optimal combination of in-house technology and know-how and external needs.

[0033] The support department can manage the progress of the project and evaluate its results. The support department, for example, manages the progress of the project and evaluates its results. Progress management and evaluation of results include, for example, progress monitoring methods and evaluation criteria, but are not limited to these examples. The support department, for example, monitors the progress of the project in real time and makes adjustments if a delay occurs. The support department can also monitor the progress of the project in real time and make adjustments if a resource shortage occurs. Furthermore, the support department can monitor the progress of the project in real time and make adjustments if it is difficult to achieve the goal. This makes it possible to manage the progress of the project and evaluate its results.

[0034] The support unit can allocate resources for a project. The support unit, for example, allocates resources for a project. Resource allocation includes, for example, resource types, allocation criteria, and the like, but is not limited to these examples. The support unit, for example, optimizes resource allocation for a project and supports efficient progress. The support unit can also optimize resource allocation for a project and support reducing resource waste. Furthermore, the support unit can optimize resource allocation for a project and provide support toward goal achievement. In this way, resources for a project can be allocated.

[0035] The collection unit can evaluate the importance of technical information and know-how from each department and prioritize collection. The collection unit, for example, evaluates the importance of technical information and know-how from each department and prioritize collection. Importance includes, but is not limited to, impact and urgency. The collection unit, for example, evaluates the impact of technical information from each department and prioritizes collection of information with high impact. The collection unit can also evaluate the uniqueness of know-how from each department and prioritize collection of know-how with high uniqueness. Furthermore, the collection unit can evaluate the update frequency of technical information from each department and prioritize collection of information with high update frequency. This enables efficient information collection by prioritizing collection of information based on importance.

[0036] The collection unit can evaluate the reliability of technical information and know-how and prioritize collecting highly reliable information. The collection unit, for example, evaluates the reliability of technical information and know-how and prioritize collecting highly reliable information. Reliability includes, for example, the source of data and past performance, but is not limited to such examples. The collection unit, for example, evaluates the source of technical information from each department and prioritize collecting information from highly reliable sources. The collection unit can also evaluate the performance of know-how from each department and prioritize collecting proven know-how. Furthermore, the collection unit can evaluate the verification results of technical information from each department and prioritize collecting verified information. In this way, highly reliable data can be ensured by preferentially collecting highly reliable information.

[0037] The collection unit can collect the latest information taking into consideration the update frequency of the technical information and know-how. The collection unit, for example, collects the latest information taking into consideration the update frequency of the technical information and know-how. The update frequency includes, for example, a data update cycle and a definition of the latest information, but is not limited to such examples. The collection unit, for example, evaluates the update history of the technical information of each department and prioritizes collecting the latest information. The collection unit can also evaluate the update frequency of the know-how of each department and prioritize collecting know-how that is updated frequently. Furthermore, the collection unit can evaluate the update timing of the technical information of each department and prioritize collecting timely information. In this way, by collecting the latest information, it is possible to always maintain the latest data.

[0038] The collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of each department. The collection unit, for example, prioritizes collecting highly relevant information by taking into account the geographical location information of each department. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the collection unit prioritizes collecting nearby technical information based on the geographical location information of each department. The collection unit can also prioritize collecting know-how specific to a region based on the geographical location information of each department. Furthermore, the collection unit can prioritize collecting information that is highly geographically relevant based on the geographical location information of each department. This enables efficient information collection by prioritizing the collection of information that is highly geographically relevant.

[0039] The collection unit can analyze the social media activities of each department and collect related technical information and know-how. The collection unit, for example, analyzes the social media activities of each department and collects related technical information and know-how. Social media activities include, but are not limited to, the content of posts and engagement rates. The collection unit, for example, analyzes the content of posts on social media by each department and collects related technical information. The collection unit can also evaluate the frequency of social media activity by each department and prioritize collecting information on departments that are more active on social media. Furthermore, the collection unit can evaluate the number of followers on social media by each department and prioritize collecting information on influential departments. This makes it possible to efficiently collect related information by analyzing social media activities.

[0040] The collection unit can customize the collection method by reflecting past feedback from each department. The collection unit, for example, customizes the collection method by reflecting past feedback from each department. Past feedback includes, for example, survey results, user reviews, etc., but is not limited to these examples. The collection unit, for example, improves the collection method based on the past feedback from each department and collects information efficiently. The collection unit can also review the collection targets and prioritize the collection of important information based on the past feedback from each department. Furthermore, the collection unit can adjust the collection timing based on the past feedback from each department and collect information at the optimal timing. In this way, the collection method can be optimized by reflecting past feedback.

[0041] The storage unit can standardize the format of the data to be stored and maintain data consistency. The storage unit, for example, standardizes the format of the data to be stored and maintains data consistency. Data formats include, but are not limited to, CSV, JSON, XML, etc. The storage unit, for example, converts technical information collected from each department into a unified format and maintains data consistency. The storage unit can also standardize know-how collected from each department and maintain data consistency. Furthermore, the storage unit can store data collected from each department in a unified format and maintain data consistency. By standardizing the data format, consistent data management is possible.

[0042] The storage unit can add meta information to data when storing the data to improve searchability. The storage unit can add meta information to data when storing the data to improve searchability, for example. Meta information includes, but is not limited to, tag information, attribute data, etc. The storage unit can add meta information to technical information collected from each department to improve searchability, for example. The storage unit can also add meta information to know-how collected from each department to improve searchability. Furthermore, the storage unit can add meta information to data collected from each department to improve searchability. By adding meta information, the searchability of the data is improved.

[0043] The storage unit can eliminate duplicate data when storing the data and perform efficient data management. The storage unit, for example, eliminates duplicate data when storing the data and performs efficient data management. Data duplication includes, but is not limited to, duplicate detection algorithms, data cleansing methods, and the like. The storage unit, for example, eliminates duplicate technical information collected from each department and performs efficient data management. The storage unit can also eliminate duplicate know-how collected from each department and perform efficient data management. Furthermore, the storage unit can eliminate duplicate data collected from each department and perform efficient data management. As a result, efficient data management is possible by eliminating duplicate data.

[0044] The storage unit can set access permissions for data when storing the data, thereby strengthening security. The storage unit, for example, sets access permissions for data when storing the data, thereby strengthening security. Access permissions include, for example, user roles, access levels, etc., but are not limited to these examples. The storage unit, for example, sets access permissions for technical information collected from each department, thereby strengthening security. The storage unit can also set access permissions for know-how collected from each department, thereby strengthening security. Furthermore, the storage unit can also set access permissions for data collected from each department, thereby strengthening security. In this way, data security is strengthened by setting access permissions.

[0045] The storage unit can determine the priority of the data to be stored and store important data with priority. The storage unit, for example, determines the priority of the data to be stored and stores important data with priority. Priorities include, but are not limited to, importance, urgency, and impact. The storage unit, for example, evaluates the importance of technical information collected from each department and stores important data with priority. The storage unit can also evaluate the importance of know-how collected from each department and store important data with priority. Furthermore, the storage unit can evaluate the importance of data collected from each department and store important data with priority. This enables efficient data management by storing important data with priority.

[0046] The storage unit can select the optimal storage method by taking into account the geographical distribution of data when storing the data. For example, the storage unit selects the optimal storage method by taking into account the geographical distribution of data when storing the data. Geographical distribution includes, but is not limited to, regional data, geographic clusters, etc. For example, the storage unit selects the optimal storage method by taking into account the geographical distribution of technical information collected from each department. The storage unit can also select the optimal storage method by taking into account the geographical distribution of know-how collected from each department. Furthermore, the storage unit can also select the optimal storage method by taking into account the geographical distribution of data collected from each department. In this way, the optimal storage method can be selected by taking into account the geographical distribution.

[0047] The storage unit can improve the accuracy of storage by referring to literature related to the data when storing the data. The storage unit, for example, improves the accuracy of storage by referring to literature related to the data when storing the data. Related literature includes, but is not limited to, academic papers, technical reports, etc. For example, the storage unit can improve the accuracy of storage by referring to literature related to technical information collected from each department. The storage unit can also improve the accuracy of storage by referring to literature related to know-how collected from each department. Furthermore, the storage unit can improve the accuracy of storage by referring to literature related to the data collected from each department. In this way, the accuracy of storage is improved by referring to related literature.

[0048] The storage unit can store data taking into consideration the market value of the data when storing the data. For example, the storage unit stores data taking into consideration the market value of the data when storing the data. Market value includes, but is not limited to, sales forecasts, demand forecasts, and the like. For example, the storage unit evaluates the market value of technical information collected from each department and stores high-value data preferentially. The storage unit can also evaluate the market value of know-how collected from each department and store high-value data preferentially. Furthermore, the storage unit can evaluate the market value of data collected from each department and store high-value data preferentially. In this way, high-value data can be stored preferentially by taking market value into consideration.

[0049] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of data during analysis. The analysis unit, for example, improves the accuracy of the analysis by taking into account the interrelationships of data during analysis. Interrelationships include, but are not limited to, correlation analysis and network analysis. For example, the analysis unit analyzes the interrelationships of technical information collected from each department and provides highly accurate results. The analysis unit can also analyze the interrelationships of know-how collected from each department and provide highly accurate results. Furthermore, the analysis unit can analyze the interrelationships of data collected from each department and provide highly accurate results. In this way, the accuracy of the analysis is improved by taking into account the interrelationships of data.

[0050] The analysis unit can perform the analysis while taking into account attribute information of the data submitter. For example, the analysis unit performs the analysis while taking into account attribute information of the data submitter. Attribute information includes, for example, age, occupation, field of expertise, etc., but is not limited to these examples. For example, the analysis unit improves the accuracy of the analysis by taking into account the field of expertise of the submitter of technical information in each department. The analysis unit can also improve the accuracy of the analysis by taking into account the experience of the submitter of know-how in each department. Furthermore, the analysis unit can improve the accuracy of the analysis by taking into account the job title of the data submitter in each department. In this way, the accuracy of the analysis is improved by taking into account the submitter's attribute information.

[0051] The analysis unit can weight the analysis based on the frequency of data submission during analysis. The analysis unit, for example, weights the analysis based on the frequency of data submission during analysis. The frequency of submission includes, for example, but is not limited to, the number of submissions and the submission interval. The analysis unit, for example, weights technical information that is frequently submitted from each department to improve the accuracy of the analysis. The analysis unit can also weight know-how that is frequently submitted from each department to improve the accuracy of the analysis. Furthermore, the analysis unit can weight data that is frequently submitted from each department to improve the accuracy of the analysis. Thus, weighting based on the frequency of submission improves the accuracy of the analysis.

[0052] The analysis unit can perform the analysis while taking into account the geographical distribution of the data. For example, the analysis unit performs the analysis while taking into account the geographical distribution of the data. Examples of geographical distribution include, but are not limited to, regional data and geographical clusters. For example, the analysis unit improves the accuracy of the analysis by taking into account the geographical distribution of technical information collected from each department. The analysis unit can also improve the accuracy of the analysis by taking into account the geographical distribution of know-how collected from each department. Furthermore, the analysis unit can improve the accuracy of the analysis by taking into account the geographical distribution of the data collected from each department. In this way, the accuracy of the analysis is improved by taking into account the geographical distribution.

[0053] The analysis unit can improve the accuracy of the analysis by referring to literature related to the data during analysis. The analysis unit, for example, improves the accuracy of the analysis by referring to literature related to the data during analysis. Related literature includes, but is not limited to, academic papers, technical reports, etc. For example, the analysis unit improves the accuracy of the analysis by referring to literature related to technical information collected from each department. The analysis unit can also improve the accuracy of the analysis by referring to literature related to know-how collected from each department. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to literature related to the data collected from each department. As a result, the accuracy of the analysis is improved by referring to related literature.

[0054] The analysis unit can perform the analysis while taking into consideration the market value of the data. For example, the analysis unit performs the analysis while taking into consideration the market value of the data. Market value includes, but is not limited to, sales forecasts and demand forecasts. For example, the analysis unit evaluates the market value of technical information collected from each department and prioritizes analysis of information with high value. The analysis unit can also evaluate the market value of know-how collected from each department and prioritize analysis of information with high value. Furthermore, the analysis unit can evaluate the market value of data collected from each department and prioritize analysis of information with high value. In this way, by taking market value into consideration, information with high value can be analyzed with priority.

[0055] The support department can monitor the progress of the project in real time while providing support and make adjustments as necessary. The support department, for example, can monitor the progress of the project in real time while providing support and make adjustments as necessary. The progress includes, but is not limited to, progress reports, real-time monitoring, and the like. The support department, for example, can monitor the progress of the project in real time and make adjustments if a delay occurs. The support department can also monitor the progress of the project in real time and make adjustments if a resource shortage occurs. Furthermore, the support department can monitor the progress of the project in real time and make adjustments if it is difficult to achieve the goal. This allows the progress of the project to be properly managed by monitoring in real time.

[0056] The support department can evaluate the results of the project at the time of support and provide feedback. For example, the support department can evaluate the results of the project at the time of support and provide feedback. Evaluation of results includes, but is not limited to, KPIs, evaluation indicators, etc. For example, the support department can evaluate the results of the project and provide feedback if it is successful. The support department can also evaluate the results of the project and provide feedback if improvement is required. Furthermore, the support department can evaluate the results of the project and provide feedback for the next project. In this way, by evaluating the results and providing feedback, the results can be utilized in the next project.

[0057] The support unit can appropriately allocate resources to a project when providing support. The support unit, for example, appropriately allocates resources to a project when providing support. Resource allocation includes, for example, resource types, allocation criteria, and the like, but is not limited to these examples. The support unit, for example, optimizes resource allocation for a project and supports efficient progress. The support unit can also optimize resource allocation for a project and support reducing resource waste. Furthermore, the support unit can optimize resource allocation for a project and provide support toward goal achievement. As a result, optimizing resource allocation enables efficient project progress.

[0058] The support department can select the optimal support method by taking into account the geographical distribution of projects when providing support. For example, the support department selects the optimal support method by taking into account the geographical distribution of projects when providing support. Geographical distribution includes, but is not limited to, regional data, geographical clusters, and the like. For example, the support department can prioritize support for nearby projects by taking into account the geographical distribution of projects. The support department can also provide a region-specific support method by taking into account the geographical distribution of projects. Furthermore, the support department can prioritize support for projects with high geographic relevance by taking into account the geographical distribution of projects. In this way, the optimal support method can be selected by taking into account the geographical distribution.

[0059] The support unit can improve the accuracy of the support by referring to related literature of the project when providing support. The support unit, for example, improves the accuracy of the support by referring to related literature of the project when providing support. Related literature includes, but is not limited to, academic papers, technical reports, etc. For example, the support unit improves the accuracy of the support by referring to related literature of the project. The support unit can also provide an optimal support method by referring to related literature of the project. Furthermore, the support unit can maximize the effectiveness of the support by referring to related literature of the project. As a result, the accuracy of the support is improved by referring to related literature.

[0060] The support department can provide support by taking into consideration the market value of the project when providing support. For example, the support department provides support by taking into consideration the market value of the project when providing support. Market value includes, but is not limited to, for example, sales forecasts and demand forecasts. For example, the support department evaluates the market value of the project and prioritizes support for high-value projects. The support department can also evaluate the market value of the project and concentrate resources on high-value projects. Furthermore, the support department can evaluate the market value of the project and provide optimal support for high-value projects. In this way, high-value projects can be prioritized for support by taking market value into consideration.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The project support system further includes a data reliability evaluation unit. The reliability evaluation unit evaluates the reliability of the collected technical information and know-how, and can provide highly reliable information to the analysis unit with priority. For example, the reliability evaluation unit evaluates reliability based on the source of the data and past performance, and selects highly reliable information. The reliability evaluation unit can also evaluate reliability based on the results of data verification, and provide verified information with priority to the analysis unit. Furthermore, the reliability evaluation unit can evaluate reliability based on the data update history, and provide the latest information with priority to the analysis unit. This allows the analysis unit to provide highly reliable information, thereby improving the accuracy of the analysis results.

[0063] The project support system further includes a data market value assessment unit. The market value assessment unit assesses the market value of the collected technical information and know-how, and can provide highly valuable information to the analysis unit on a priority basis. For example, the market value assessment unit assesses market value based on sales forecasts and demand forecasts, and selects highly valuable information. The market value assessment unit can also assess market value based on competitive analysis, and provide competitive information on a priority basis to the analysis unit. Furthermore, the market value assessment unit can assess market value based on customer feedback, and provide information with high customer satisfaction on a priority basis to the analysis unit. In this way, by providing information with high market value to the analysis unit, analysis results with high business value can be obtained.

[0064] The project support system further includes a data geographic distribution evaluation unit. The geographic distribution evaluation unit evaluates the geographic distribution of the collected technical information and know-how and can provide region-specific information preferentially to the analysis unit. For example, the geographic distribution evaluation unit evaluates the geographic distribution based on region-specific data and selects region-specific technical information. The geographic distribution evaluation unit can also evaluate the geographic distribution based on geographic clusters and provide region-specific information with high relevance preferentially to the analysis unit. Furthermore, the geographic distribution evaluation unit can evaluate the geographic distribution based on regional market trends and provide region-specific information with high market value preferentially to the analysis unit. In this way, by providing information that takes geographic distribution into account to the analysis unit, analysis results that meet region-specific needs can be obtained.

[0065] The project support system further includes a data update frequency evaluation unit. The update frequency evaluation unit evaluates the update frequency of the collected technical information and know-how, and can provide the latest information to the analysis unit with priority. For example, the update frequency evaluation unit evaluates the update frequency based on the data update history and selects information that is updated frequently. The update frequency evaluation unit can also evaluate the update frequency based on the data update cycle and provide the latest information to the analysis unit with priority. Furthermore, the update frequency evaluation unit can evaluate the update frequency based on the timing of data updates and provide the timely information to the analysis unit with priority. In this way, by providing the latest information to the analysis unit, analysis results based on the latest data can always be obtained.

[0066] The project support system further includes a data interrelationship evaluation unit. The interrelationship evaluation unit evaluates the interrelationships of the collected technical information and know-how, and can provide highly relevant information to the analysis unit with priority. For example, the interrelationship evaluation unit evaluates the interrelationships based on correlation analysis and selects highly relevant information. The interrelationship evaluation unit can also evaluate the interrelationships based on network analysis and provide highly relevant information to the analysis unit with priority. Furthermore, the interrelationship evaluation unit can evaluate the interrelationships based on the interdependence of data, and provide highly relevant information to the analysis unit with priority. In this way, by providing information that takes interrelationships into consideration to the analysis unit, highly accurate analysis results can be obtained.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The Collection Department collects data on internal technology and know-how. Internal technology and know-how includes specific technical fields, business processes, and specialized knowledge. The Collection Department collects technical information and know-how from each department, technology needs and market research data from external companies, and feedback from customers. Step 2: The storage unit stores the data collected by the collection unit in a vector database. The vector database includes text data, numerical data, image data, etc. The storage unit stores the collected technical information, know-how, and data on external needs in the vector database. Step 3: The analysis unit analyzes the data stored in the vector database and performs optimal matching. The analysis unit uses generation AI to find the optimal combination of in-house technology and know-how with external needs. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to calculate the similarity between in-house technology and know-how and external needs, and performs optimal matching. Step 4: The Support Department supports the creation of projects based on the matching results obtained by the Analysis Department. The Support Department manages the progress of the project, evaluates its results, and allocates resources. The Support Department monitors the progress of the project in real time and makes adjustments as necessary.

[0069] (Example 2) A project development support system according to an embodiment of the present invention uses a generative AI-based vector database to match internal technology and know-how within a large company with external needs and support project development. The project development support system collects data on internal technology and know-how and stores it in the vector database. Next, it collects data on external needs and stores it in the vector database. The generative AI analyzes this data and performs optimal matching. Based on the matching results, it supports project development. This system breaks down the siloed structure within large companies and enables effective utilization of internal technology and know-how. Furthermore, as the number of projects increases, companies are expected to become innovation hubs for new businesses. For example, the project development support system collects data on internal technology and know-how and stores it in the vector database. In this process, detailed technical information and know-how from each department is collected and stored in the vector database. For example, documents, reports, and research data related to specific technologies are collected. Next, the project development support system collects data on external needs and stores it in the vector database. For example, it collects technology needs from external companies, market research data, and customer feedback. The generative AI analyzes this data and performs optimal matching. The generative AI analyzes data stored in a vector database to find the optimal combination of in-house technology and know-how with external needs. For example, if a specific technology matches the needs of an external company, it will be turned into a project. The project creation support system then supports project creation based on the matching results. Specifically, it supports project progress management, outcome evaluation, resource allocation, and more. For example, it monitors project progress in real time and makes adjustments as needed. This system breaks down the siloed structure within large companies and enables the effective use of in-house technology and know-how. Furthermore, as the number of projects increases, companies are expected to become innovation hubs for new businesses. This allows the project creation support system to optimally match in-house technology and know-how with external needs and support project creation.

[0070] A project support system according to an embodiment includes a collection unit, a storage unit, an analysis unit, and a support unit. The collection unit collects data on in-house technology and know-how. Examples of in-house technology and know-how include, but are not limited to, specific technical fields, business processes, and specialized knowledge. The collection unit collects, for example, technical information and know-how from each department. The collection unit can also collect technology needs and market research data from external companies. For example, the collection unit can collect technology needs and market research data from external companies, customer feedback, and the like. The storage unit stores the data collected by the collection unit in a vector database. Examples of the vector database include, but are not limited to, text data, numerical data, and image data. The storage unit stores, for example, the collected technical information and know-how in the vector database. The storage unit can also store collected data on external needs in the vector database. The analysis unit analyzes the data stored in the vector database and performs optimal matching. The analysis unit, for example, uses a generative AI to find the optimal combination of in-house technology and know-how and external needs. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI calculates the similarity between in-house technology and know-how and external needs to perform optimal matching. The analysis unit can also use the generation AI to analyze the relevance between in-house technology and know-how and external needs. The support unit supports project creation based on the matching results obtained by the analysis unit. The support unit, for example, manages project progress and evaluates results. The support unit can also allocate project resources. For example, the support unit monitors the progress of the project in real time and makes adjustments as necessary. As a result, the project creation support system according to the embodiment can optimally match in-house technology and know-how with external needs to support project creation.

[0071] The collection unit can collect technical information and know-how from each department. The collection unit, for example, collects technical information and know-how from each department. The technical information and know-how includes, for example, specific technical fields, business processes, and specialized knowledge, but is not limited to these examples. The collection unit, for example, evaluates the impact of the technical information from each department and prioritizes collecting information with high impact. The collection unit can also evaluate the uniqueness of the know-how from each department and prioritize collecting know-how with high uniqueness. Furthermore, the collection unit can evaluate the update frequency of the technical information from each department and prioritize collecting information with high update frequency. This allows for effective collection of technical information and know-how from each department.

[0072] The collection unit can collect technology needs and market research data from external companies. The collection unit, for example, collects technology needs and market research data from external companies. The technology needs and market research data include, but are not limited to, specific technology fields, market trends, and customer needs. The collection unit, for example, collects technology needs from external companies and stores them in a vector database. The collection unit can also collect market research data and store it in the vector database. Furthermore, the collection unit can collect feedback from customers and store it in the vector database. This makes it possible to effectively collect technology needs and market research data from external companies.

[0073] The analysis unit can analyze data stored in the vector database to find an appropriate combination of in-house technology and know-how and external needs. The analysis unit can, for example, analyze data stored in the vector database to find an appropriate combination of in-house technology and know-how and external needs. Examples of appropriate combinations include, but are not limited to, similarity calculation methods and matching algorithms. The analysis unit can, for example, use a generation AI to calculate the similarity between in-house technology and know-how and external needs. The analysis unit can also use the generation AI to analyze the relevance between in-house technology and know-how and external needs. Furthermore, the analysis unit can also use the generation AI to find the optimal combination of in-house technology and know-how and external needs. This makes it possible to find the optimal combination of in-house technology and know-how and external needs.

[0074] The support department can manage the progress of the project and evaluate its results. The support department, for example, manages the progress of the project and evaluates its results. Progress management and evaluation of results include, for example, progress monitoring methods and evaluation criteria, but are not limited to these examples. The support department, for example, monitors the progress of the project in real time and makes adjustments if a delay occurs. The support department can also monitor the progress of the project in real time and make adjustments if a resource shortage occurs. Furthermore, the support department can monitor the progress of the project in real time and make adjustments if it is difficult to achieve the goal. This makes it possible to manage the progress of the project and evaluate its results.

[0075] The support unit can allocate resources for a project. The support unit, for example, allocates resources for a project. Resource allocation includes, for example, resource types, allocation criteria, and the like, but is not limited to these examples. The support unit, for example, optimizes resource allocation for a project and supports efficient progress. The support unit can also optimize resource allocation for a project and support reducing resource waste. Furthermore, the support unit can optimize resource allocation for a project and provide support toward goal achievement. In this way, resources for a project can be allocated.

[0076] The project support system includes a collection unit that estimates a user's emotions and adjusts the timing of collecting technical information and know-how based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of collecting technical information and know-how based on the estimated user emotions. User emotions include, but are not limited to, stress, concentration, fatigue, and the like. For example, when the user is stressed, the collection unit delays the collection timing to collect information when the user is relaxed. Furthermore, when the user is concentrating, the collection unit can also advance the collection timing to efficiently collect information. Furthermore, when the user is tired, the collection unit can adjust the collection timing to collect information after the user has rested. This enables efficient information collection by adjusting the collection timing based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The collection unit can evaluate the importance of technical information and know-how from each department and prioritize collection. The collection unit, for example, evaluates the importance of technical information and know-how from each department and prioritize collection. Importance includes, but is not limited to, impact and urgency. The collection unit, for example, evaluates the impact of technical information from each department and prioritizes collection of information with high impact. The collection unit can also evaluate the uniqueness of know-how from each department and prioritize collection of know-how with high uniqueness. Furthermore, the collection unit can evaluate the update frequency of technical information from each department and prioritize collection of information with high update frequency. This enables efficient information collection by prioritizing collection of information based on importance.

[0078] The collection unit can evaluate the reliability of technical information and know-how and prioritize collecting highly reliable information. The collection unit, for example, evaluates the reliability of technical information and know-how and prioritize collecting highly reliable information. Reliability includes, for example, the source of data and past performance, but is not limited to such examples. The collection unit, for example, evaluates the source of technical information from each department and prioritize collecting information from highly reliable sources. The collection unit can also evaluate the performance of know-how from each department and prioritize collecting proven know-how. Furthermore, the collection unit can evaluate the verification results of technical information from each department and prioritize collecting verified information. In this way, highly reliable data can be ensured by preferentially collecting highly reliable information.

[0079] The collection unit can collect the latest information taking into consideration the update frequency of the technical information and know-how. The collection unit, for example, collects the latest information taking into consideration the update frequency of the technical information and know-how. The update frequency includes, for example, a data update cycle and a definition of the latest information, but is not limited to such examples. The collection unit, for example, evaluates the update history of the technical information of each department and prioritizes collecting the latest information. The collection unit can also evaluate the update frequency of the know-how of each department and prioritize collecting know-how that is updated frequently. Furthermore, the collection unit can evaluate the update timing of the technical information of each department and prioritize collecting timely information. In this way, by collecting the latest information, it is possible to always maintain the latest data.

[0080] The project support system includes a collection unit that estimates a user's emotions and determines the priority of technical information and know-how to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of technical information and know-how to be collected based on the estimated user emotions. User emotions include, but are not limited to, excitement, relaxation, stress, etc. For example, when the user is excited, the collection unit prioritizes collecting innovative technical information. Furthermore, when the user is relaxed, the collection unit can prioritize collecting basic know-how. Furthermore, when the user is stressed, the collection unit can prioritize collecting easy-to-understand technical information. This enables efficient information collection by determining the priority of information based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of each department. The collection unit, for example, prioritizes collecting highly relevant information by taking into account the geographical location information of each department. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the collection unit prioritizes collecting nearby technical information based on the geographical location information of each department. The collection unit can also prioritize collecting know-how specific to a region based on the geographical location information of each department. Furthermore, the collection unit can prioritize collecting information that is highly geographically relevant based on the geographical location information of each department. This enables efficient information collection by prioritizing the collection of information that is highly geographically relevant.

[0082] The collection unit can analyze the social media activities of each department and collect related technical information and know-how. The collection unit, for example, analyzes the social media activities of each department and collects related technical information and know-how. Social media activities include, but are not limited to, the content of posts and engagement rates. The collection unit, for example, analyzes the content of posts on social media by each department and collects related technical information. The collection unit can also evaluate the frequency of social media activity by each department and prioritize collecting information on departments that are more active on social media. Furthermore, the collection unit can evaluate the number of followers on social media by each department and prioritize collecting information on influential departments. This makes it possible to efficiently collect related information by analyzing social media activities.

[0083] The collection unit can customize the collection method by reflecting past feedback from each department. The collection unit, for example, customizes the collection method by reflecting past feedback from each department. Past feedback includes, for example, survey results, user reviews, etc., but is not limited to these examples. The collection unit, for example, improves the collection method based on the past feedback from each department and collects information efficiently. The collection unit can also review the collection targets and prioritize the collection of important information based on the past feedback from each department. Furthermore, the collection unit can adjust the collection timing based on the past feedback from each department and collect information at the optimal timing. In this way, the collection method can be optimized by reflecting past feedback.

[0084] The storage unit can standardize the format of the data to be stored and maintain data consistency. The storage unit, for example, standardizes the format of the data to be stored and maintains data consistency. Data formats include, but are not limited to, CSV, JSON, XML, etc. The storage unit, for example, converts technical information collected from each department into a unified format and maintains data consistency. The storage unit can also standardize know-how collected from each department and maintain data consistency. Furthermore, the storage unit can store data collected from each department in a unified format and maintain data consistency. By standardizing the data format, consistent data management is possible.

[0085] The storage unit can add meta information to data when storing the data to improve searchability. The storage unit can add meta information to data when storing the data to improve searchability, for example. Meta information includes, but is not limited to, tag information, attribute data, etc. The storage unit can add meta information to technical information collected from each department to improve searchability, for example. The storage unit can also add meta information to know-how collected from each department to improve searchability. Furthermore, the storage unit can add meta information to data collected from each department to improve searchability. By adding meta information, the searchability of the data is improved.

[0086] The storage unit can eliminate duplicate data when storing the data and perform efficient data management. The storage unit, for example, eliminates duplicate data when storing the data and performs efficient data management. Data duplication includes, but is not limited to, duplicate detection algorithms, data cleansing methods, and the like. The storage unit, for example, eliminates duplicate technical information collected from each department and performs efficient data management. The storage unit can also eliminate duplicate know-how collected from each department and perform efficient data management. Furthermore, the storage unit can eliminate duplicate data collected from each department and perform efficient data management. As a result, efficient data management is possible by eliminating duplicate data.

[0087] The storage unit can set access permissions for data when storing the data, thereby strengthening security. The storage unit, for example, sets access permissions for data when storing the data, thereby strengthening security. Access permissions include, for example, user roles, access levels, etc., but are not limited to these examples. The storage unit, for example, sets access permissions for technical information collected from each department, thereby strengthening security. The storage unit can also set access permissions for know-how collected from each department, thereby strengthening security. Furthermore, the storage unit can also set access permissions for data collected from each department, thereby strengthening security. In this way, data security is strengthened by setting access permissions.

[0088] The storage unit can determine the priority of the data to be stored and store important data with priority. The storage unit, for example, determines the priority of the data to be stored and stores important data with priority. Priorities include, but are not limited to, importance, urgency, and impact. The storage unit, for example, evaluates the importance of technical information collected from each department and stores important data with priority. The storage unit can also evaluate the importance of know-how collected from each department and store important data with priority. Furthermore, the storage unit can evaluate the importance of data collected from each department and store important data with priority. This enables efficient data management by storing important data with priority.

[0089] The storage unit can select the optimal storage method by taking into account the geographical distribution of data when storing the data. For example, the storage unit selects the optimal storage method by taking into account the geographical distribution of data when storing the data. Geographical distribution includes, but is not limited to, regional data, geographic clusters, etc. For example, the storage unit selects the optimal storage method by taking into account the geographical distribution of technical information collected from each department. The storage unit can also select the optimal storage method by taking into account the geographical distribution of know-how collected from each department. Furthermore, the storage unit can also select the optimal storage method by taking into account the geographical distribution of data collected from each department. In this way, the optimal storage method can be selected by taking into account the geographical distribution.

[0090] The storage unit can improve the accuracy of storage by referring to literature related to the data when storing the data. The storage unit, for example, improves the accuracy of storage by referring to literature related to the data when storing the data. Related literature includes, but is not limited to, academic papers, technical reports, etc. For example, the storage unit can improve the accuracy of storage by referring to literature related to technical information collected from each department. The storage unit can also improve the accuracy of storage by referring to literature related to know-how collected from each department. Furthermore, the storage unit can improve the accuracy of storage by referring to literature related to the data collected from each department. In this way, the accuracy of storage is improved by referring to related literature.

[0091] The storage unit can store data taking into consideration the market value of the data when storing the data. For example, the storage unit stores data taking into consideration the market value of the data when storing the data. Market value includes, but is not limited to, sales forecasts, demand forecasts, and the like. For example, the storage unit evaluates the market value of technical information collected from each department and stores high-value data preferentially. The storage unit can also evaluate the market value of know-how collected from each department and store high-value data preferentially. Furthermore, the storage unit can evaluate the market value of data collected from each department and store high-value data preferentially. In this way, high-value data can be stored preferentially by taking market value into consideration.

[0092] The project support system includes an analysis unit that estimates a user's emotions and adjusts analysis criteria based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts analysis criteria based on the estimated user emotions. User emotions include, but are not limited to, relaxation, hurry, and excitement. For example, when the user is relaxed, the analysis unit performs a detailed analysis to provide highly accurate results. When the user is in a hurry, the analysis unit can also perform a simplified analysis to provide quick results. Furthermore, when the user is excited, the analysis unit can provide visually easy-to-understand analysis results. Thus, by adjusting the analysis criteria based on the user's emotions, appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of data during analysis. The analysis unit, for example, improves the accuracy of the analysis by taking into account the interrelationships of data during analysis. Interrelationships include, but are not limited to, correlation analysis and network analysis. For example, the analysis unit analyzes the interrelationships of technical information collected from each department and provides highly accurate results. The analysis unit can also analyze the interrelationships of know-how collected from each department and provide highly accurate results. Furthermore, the analysis unit can analyze the interrelationships of data collected from each department and provide highly accurate results. In this way, the accuracy of the analysis is improved by taking into account the interrelationships of data.

[0094] The analysis unit can perform the analysis while taking into account attribute information of the data submitter. For example, the analysis unit performs the analysis while taking into account attribute information of the data submitter. Attribute information includes, for example, age, occupation, field of expertise, etc., but is not limited to these examples. For example, the analysis unit improves the accuracy of the analysis by taking into account the field of expertise of the submitter of technical information in each department. The analysis unit can also improve the accuracy of the analysis by taking into account the experience of the submitter of know-how in each department. Furthermore, the analysis unit can improve the accuracy of the analysis by taking into account the job title of the data submitter in each department. In this way, the accuracy of the analysis is improved by taking into account the submitter's attribute information.

[0095] The analysis unit can weight the analysis based on the frequency of data submission during analysis. The analysis unit, for example, weights the analysis based on the frequency of data submission during analysis. The frequency of submission includes, for example, but is not limited to, the number of submissions and the submission interval. The analysis unit, for example, weights technical information that is frequently submitted from each department to improve the accuracy of the analysis. The analysis unit can also weight know-how that is frequently submitted from each department to improve the accuracy of the analysis. Furthermore, the analysis unit can weight data that is frequently submitted from each department to improve the accuracy of the analysis. Thus, weighting based on the frequency of submission improves the accuracy of the analysis.

[0096] The project support system includes an analysis unit that estimates a user's emotion and adjusts the display order of analysis results based on the estimated user emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display order of analysis results based on the estimated user emotion. User emotions include, but are not limited to, relaxation, hurry, and excitement. For example, when the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can prioritize displaying analysis results that focus on the main points. Furthermore, when the user is excited, the analysis unit can prioritize displaying analysis results that are visually easy to understand. In this way, appropriate analysis results can be provided by adjusting the display order based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The analysis unit can perform the analysis while taking into account the geographical distribution of the data. For example, the analysis unit performs the analysis while taking into account the geographical distribution of the data. Examples of geographical distribution include, but are not limited to, regional data and geographical clusters. For example, the analysis unit improves the accuracy of the analysis by taking into account the geographical distribution of technical information collected from each department. The analysis unit can also improve the accuracy of the analysis by taking into account the geographical distribution of know-how collected from each department. Furthermore, the analysis unit can improve the accuracy of the analysis by taking into account the geographical distribution of the data collected from each department. In this way, the accuracy of the analysis is improved by taking into account the geographical distribution.

[0098] The analysis unit can improve the accuracy of the analysis by referring to literature related to the data during analysis. The analysis unit, for example, improves the accuracy of the analysis by referring to literature related to the data during analysis. Related literature includes, but is not limited to, academic papers, technical reports, etc. For example, the analysis unit improves the accuracy of the analysis by referring to literature related to technical information collected from each department. The analysis unit can also improve the accuracy of the analysis by referring to literature related to know-how collected from each department. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to literature related to the data collected from each department. As a result, the accuracy of the analysis is improved by referring to related literature.

[0099] The analysis unit can perform the analysis while taking into consideration the market value of the data. For example, the analysis unit performs the analysis while taking into consideration the market value of the data. Market value includes, but is not limited to, sales forecasts and demand forecasts. For example, the analysis unit evaluates the market value of technical information collected from each department and prioritizes analysis of information with high value. The analysis unit can also evaluate the market value of know-how collected from each department and prioritize analysis of information with high value. Furthermore, the analysis unit can evaluate the market value of data collected from each department and prioritize analysis of information with high value. In this way, by taking market value into consideration, information with high value can be analyzed with priority.

[0100] The project support system includes a support unit that estimates a user's emotions and adjusts a project support method based on the estimated user emotions. The support unit, for example, estimates the user's emotions and adjusts the project support method based on the estimated user emotions. User emotions include, but are not limited to, relaxation, hurry, excitement, and the like. For example, when the user is relaxed, the support unit can provide a detailed support method. When the user is in a hurry, the support unit can also provide a simplified support method. Furthermore, when the user is excited, the support unit can also provide a visually easy-to-understand support method. In this way, appropriate support can be provided by adjusting the support method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The support department can monitor the progress of the project in real time while providing support and make adjustments as necessary. The support department, for example, can monitor the progress of the project in real time while providing support and make adjustments as necessary. The progress includes, but is not limited to, progress reports, real-time monitoring, and the like. The support department, for example, can monitor the progress of the project in real time and make adjustments if a delay occurs. The support department can also monitor the progress of the project in real time and make adjustments if a resource shortage occurs. Furthermore, the support department can monitor the progress of the project in real time and make adjustments if it is difficult to achieve the goal. This allows the progress of the project to be properly managed by monitoring in real time.

[0102] The support department can evaluate the results of the project at the time of support and provide feedback. For example, the support department can evaluate the results of the project at the time of support and provide feedback. Evaluation of results includes, but is not limited to, KPIs, evaluation indicators, etc. For example, the support department can evaluate the results of the project and provide feedback if it is successful. The support department can also evaluate the results of the project and provide feedback if improvement is required. Furthermore, the support department can evaluate the results of the project and provide feedback for the next project. In this way, by evaluating the results and providing feedback, the results can be utilized in the next project.

[0103] The support unit can appropriately allocate resources to a project when providing support. The support unit, for example, appropriately allocates resources to a project when providing support. Resource allocation includes, for example, resource types, allocation criteria, and the like, but is not limited to these examples. The support unit, for example, optimizes resource allocation for a project and supports efficient progress. The support unit can also optimize resource allocation for a project and support reducing resource waste. Furthermore, the support unit can optimize resource allocation for a project and provide support toward goal achievement. As a result, optimizing resource allocation enables efficient project progress.

[0104] The project creation support system includes a support unit that estimates a user's emotions and determines the priority of projects based on the estimated user emotions. The support unit, for example, estimates the user's emotions and determines the priority of projects based on the estimated user emotions. User emotions include, but are not limited to, excitement, relaxation, stress, etc. For example, when the user is excited, the support unit prioritizes innovative projects. Furthermore, when the user is relaxed, the support unit can prioritize basic projects. Furthermore, when the user is stressed, the support unit can prioritize easy projects. This enables appropriate project creation by determining the priority based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] The support department can select the optimal support method by taking into account the geographical distribution of projects when providing support. For example, the support department selects the optimal support method by taking into account the geographical distribution of projects when providing support. Geographical distribution includes, but is not limited to, regional data, geographical clusters, and the like. For example, the support department can prioritize support for nearby projects by taking into account the geographical distribution of projects. The support department can also provide a region-specific support method by taking into account the geographical distribution of projects. Furthermore, the support department can prioritize support for projects with high geographic relevance by taking into account the geographical distribution of projects. In this way, the optimal support method can be selected by taking into account the geographical distribution.

[0106] The support unit can improve the accuracy of the support by referring to related literature of the project when providing support. The support unit, for example, improves the accuracy of the support by referring to related literature of the project when providing support. Related literature includes, but is not limited to, academic papers, technical reports, etc. For example, the support unit improves the accuracy of the support by referring to related literature of the project. The support unit can also provide an optimal support method by referring to related literature of the project. Furthermore, the support unit can maximize the effectiveness of the support by referring to related literature of the project. As a result, the accuracy of the support is improved by referring to related literature.

[0107] The support department can provide support by taking into consideration the market value of the project when providing support. For example, the support department provides support by taking into consideration the market value of the project when providing support. Market value includes, but is not limited to, for example, sales forecasts and demand forecasts. For example, the support department evaluates the market value of the project and prioritizes support for high-value projects. The support department can also evaluate the market value of the project and concentrate resources on high-value projects. Furthermore, the support department can evaluate the market value of the project and provide optimal support for high-value projects. In this way, high-value projects can be prioritized for support by taking market value into consideration. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, storage unit, analysis unit, and support unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects technical information and know-how using the camera 42 and microphone 38B of the smart device 14 and transmits the information to the data processing device 12 via the control unit 46A. The storage unit stores the collected data in a vector database, for example, by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the data using a generation AI by the specific processing unit 290 of the data processing device 12, for example, and performs optimal matching. The support unit manages the progress of the project and evaluates its results, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, storage unit, analysis unit, and support unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects technical information and know-how using the camera 42 and microphone 238 of the smart glasses 214 and transmits the information to the data processing device 12 via the control unit 46A. The storage unit stores the collected data in a vector database, for example, by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the data using a generated AI by the specific processing unit 290 of the data processing device 12, for example, and performs optimal matching. The support unit manages the progress of the project and evaluates its results, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, storage unit, analysis unit, and support unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects technical information and know-how using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the information to the data processing device 12 via the control unit 46A. The storage unit stores the data collected by, for example, the specific processing unit 290 of the data processing device 12 in a vector database. The analysis unit analyzes the data using a generation AI by, for example, the specific processing unit 290 of the data processing device 12, and performs optimal matching. The support unit manages the progress of the project and evaluates its results by, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, storage unit, analysis unit, and support unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects technical information and know-how using the camera 42 and microphone 238 of the robot 414 and transmits the information to the data processing device 12 via the control unit 46A. The storage unit stores the data collected by, for example, the specific processing unit 290 of the data processing device 12 in a vector database. The analysis unit analyzes the data using a generation AI by, for example, the specific processing unit 290 of the data processing device 12, and performs optimal matching. The support unit manages the progress of the project and evaluates its results by, for example, the specific processing unit 290 of the data processing device 12.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The project support system includes a collection unit that estimates a user's emotions and adjusts the timing of collecting technical information and know-how based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of collecting technical information and know-how based on the estimated user emotions. User emotions include, but are not limited to, stress, concentration, fatigue, and the like. For example, when the user is stressed, the collection unit delays the collection timing to collect information when the user is relaxed. Furthermore, when the user is concentrating, the collection unit can also advance the collection timing to efficiently collect information. Furthermore, when the user is tired, the collection unit can adjust the collection timing to collect information after the user has rested. This enables efficient information collection by adjusting the collection timing based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0110] The project support system includes a collection unit that estimates a user's emotions and determines the priority of technical information and know-how to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of technical information and know-how to be collected based on the estimated user emotions. User emotions include, but are not limited to, excitement, relaxation, stress, etc. For example, when the user is excited, the collection unit prioritizes collecting innovative technical information. Furthermore, when the user is relaxed, the collection unit can prioritize collecting basic know-how. Furthermore, when the user is stressed, the collection unit can prioritize collecting easy-to-understand technical information. This enables efficient information collection by determining the priority of information based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0111] The project support system includes an analysis unit that estimates a user's emotions and adjusts analysis criteria based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts analysis criteria based on the estimated user emotions. User emotions include, but are not limited to, relaxation, hurry, and excitement. For example, when the user is relaxed, the analysis unit performs a detailed analysis to provide highly accurate results. When the user is in a hurry, the analysis unit can also perform a simplified analysis to provide quick results. Furthermore, when the user is excited, the analysis unit can provide visually easy-to-understand analysis results. Thus, by adjusting the analysis criteria based on the user's emotions, appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0112] The project support system includes an analysis unit that estimates a user's emotion and adjusts the display order of analysis results based on the estimated user emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display order of analysis results based on the estimated user emotion. User emotions include, but are not limited to, relaxation, hurry, and excitement. For example, when the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can prioritize displaying analysis results that focus on the main points. Furthermore, when the user is excited, the analysis unit can prioritize displaying analysis results that are visually easy to understand. In this way, appropriate analysis results can be provided by adjusting the display order based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0113] The project support system includes a support unit that estimates a user's emotions and adjusts a project support method based on the estimated user emotions. The support unit, for example, estimates the user's emotions and adjusts the project support method based on the estimated user emotions. User emotions include, but are not limited to, relaxation, hurry, excitement, and the like. For example, when the user is relaxed, the support unit can provide a detailed support method. When the user is in a hurry, the support unit can also provide a simplified support method. Furthermore, when the user is excited, the support unit can also provide a visually easy-to-understand support method. In this way, appropriate support can be provided by adjusting the support method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0114] The project support system further includes a data reliability evaluation unit. The reliability evaluation unit evaluates the reliability of the collected technical information and know-how, and can provide highly reliable information to the analysis unit with priority. For example, the reliability evaluation unit evaluates reliability based on the source of the data and past performance, and selects highly reliable information. The reliability evaluation unit can also evaluate reliability based on the results of data verification, and provide verified information with priority to the analysis unit. Furthermore, the reliability evaluation unit can evaluate reliability based on the data update history, and provide the latest information with priority to the analysis unit. This allows the analysis unit to provide highly reliable information, thereby improving the accuracy of the analysis results.

[0115] The project support system further includes a data market value assessment unit. The market value assessment unit assesses the market value of the collected technical information and know-how, and can provide highly valuable information to the analysis unit on a priority basis. For example, the market value assessment unit assesses market value based on sales forecasts and demand forecasts, and selects highly valuable information. The market value assessment unit can also assess market value based on competitive analysis, and provide competitive information on a priority basis to the analysis unit. Furthermore, the market value assessment unit can assess market value based on customer feedback, and provide information with high customer satisfaction on a priority basis to the analysis unit. In this way, by providing information with high market value to the analysis unit, analysis results with high business value can be obtained.

[0116] The project support system further includes a data geographic distribution evaluation unit. The geographic distribution evaluation unit evaluates the geographic distribution of the collected technical information and know-how and can provide region-specific information preferentially to the analysis unit. For example, the geographic distribution evaluation unit evaluates the geographic distribution based on region-specific data and selects region-specific technical information. The geographic distribution evaluation unit can also evaluate the geographic distribution based on geographic clusters and provide region-specific information with high relevance preferentially to the analysis unit. Furthermore, the geographic distribution evaluation unit can evaluate the geographic distribution based on regional market trends and provide region-specific information with high market value preferentially to the analysis unit. In this way, by providing information that takes geographic distribution into account to the analysis unit, analysis results that meet region-specific needs can be obtained.

[0117] The project support system further includes a data update frequency evaluation unit. The update frequency evaluation unit evaluates the update frequency of the collected technical information and know-how, and can provide the latest information to the analysis unit with priority. For example, the update frequency evaluation unit evaluates the update frequency based on the data update history and selects information that is updated frequently. The update frequency evaluation unit can also evaluate the update frequency based on the data update cycle and provide the latest information to the analysis unit with priority. Furthermore, the update frequency evaluation unit can evaluate the update frequency based on the timing of data updates and provide the timely information to the analysis unit with priority. In this way, by providing the latest information to the analysis unit, analysis results based on the latest data can always be obtained.

[0118] The project support system further includes a data interrelationship evaluation unit. The interrelationship evaluation unit evaluates the interrelationships of the collected technical information and know-how, and can provide highly relevant information to the analysis unit with priority. For example, the interrelationship evaluation unit evaluates the interrelationships based on correlation analysis and selects highly relevant information. The interrelationship evaluation unit can also evaluate the interrelationships based on network analysis and provide highly relevant information to the analysis unit with priority. Furthermore, the interrelationship evaluation unit can evaluate the interrelationships based on the interdependence of data, and provide highly relevant information to the analysis unit with priority. In this way, by providing information that takes interrelationships into consideration to the analysis unit, highly accurate analysis results can be obtained.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The Collection Department collects data on internal technology and know-how. Internal technology and know-how includes specific technical fields, business processes, and specialized knowledge. The Collection Department collects technical information and know-how from each department, technology needs and market research data from external companies, and feedback from customers. Step 2: The storage unit stores the data collected by the collection unit in a vector database. The vector database includes text data, numerical data, image data, etc. The storage unit stores the collected technical information, know-how, and data on external needs in the vector database. Step 3: The analysis unit analyzes the data stored in the vector database and performs optimal matching. The analysis unit uses generation AI to find the optimal combination of in-house technology and know-how with external needs. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to calculate the similarity between in-house technology and know-how and external needs, and performs optimal matching. Step 4: The Support Department supports the creation of projects based on the matching results obtained by the Analysis Department. The Support Department manages the progress of the project, evaluates its results, and allocates resources. The Support Department monitors the progress of the project in real time and makes adjustments as necessary.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0164] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0165] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0166] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0168] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0169] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0177] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0178] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0182] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0183] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0184] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0185] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0186] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0187] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0190] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0192] [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects data on in-house technology and know-how, a storage unit that stores the data collected by the collection unit in a vector database; an analysis unit that analyzes the data stored by the storage unit and performs appropriate matching; a support unit that supports project creation based on the matching results obtained by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Collect technical information and know-how from each department 2. The system of claim 1.

3. The collecting unit Collecting technology needs and market research data from external companies 2. The system of claim 1.

4. The analysis unit Analyze data stored in a vector database to find the right combination of in-house technology and know-how with external needs 2. The system of claim 1.

5. The support unit Manage project progress and evaluate results 2. The system of claim 1.

6. The support unit Allocate resources for the project 2. The system of claim 1.

7. The collecting unit Estimate user emotions and adjust the timing of collecting technical information and know-how based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Evaluate the importance of technical information and know-how from each department and collect them in order of priority.

2. The system of claim 1.

9. The collecting unit Evaluate the reliability of technical information and know-how, and prioritize collection of highly reliable information 2. The system of claim 1.

10. The collecting unit Collect the latest information, taking into account the frequency of updates to technical information and know-how.

2. The system of claim 1.

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